Application of Artificial Intelligence in Radiography: A Comparative Analysis of Image Interpretation Accuracy

Thesis Abstract

Abstract
The field of radiography has witnessed significant advancements with the integration of artificial intelligence (AI) technologies in medical imaging processes. This thesis explores the Application of Artificial Intelligence in Radiography, focusing on a Comparative Analysis of Image Interpretation Accuracy. The primary objective is to evaluate the effectiveness of AI algorithms in improving the accuracy and efficiency of image interpretation in radiography compared to traditional methods. The study begins with an in-depth review of the background of AI in radiography, emphasizing the potential benefits and challenges associated with its implementation. The problem statement highlights the existing limitations in current image interpretation practices and the need for more accurate and efficient solutions. The objectives of the study include assessing the performance of AI algorithms in image interpretation, identifying the limitations of AI technology in radiography, and proposing recommendations for enhancing its effectiveness. The research methodology employed in this study encompasses a comprehensive literature review of existing studies on AI in radiography, data collection from relevant sources, and the application of statistical analysis to compare the performance of AI algorithms with traditional methods. The study also includes the use of qualitative interviews to gather insights from radiography professionals regarding their experiences with AI technology. The findings of the study reveal a significant improvement in image interpretation accuracy and efficiency with the use of AI algorithms compared to traditional methods. The comparative analysis highlights the strengths and limitations of AI technology in radiography, providing valuable insights for healthcare professionals and policymakers. The discussion of findings delves into the implications of these results for the future implementation of AI in radiography practice. In conclusion, this thesis underscores the significance of integrating AI technologies in radiography to enhance image interpretation accuracy and efficiency. The study contributes to the growing body of knowledge on the application of AI in healthcare settings, particularly in the field of radiography. The recommendations put forth in this thesis aim to guide future research and practical implementation of AI solutions in radiography practice, ultimately improving patient outcomes and healthcare delivery.